{"id":26618,"date":"2022-05-25T08:16:01","date_gmt":"2022-05-25T13:16:01","guid":{"rendered":"https:\/\/saluddigital.com\/?p=26618"},"modified":"2025-10-20T11:53:38","modified_gmt":"2025-10-20T17:53:38","slug":"desarrollan-herramienta-de-inteligencia-artificial-que-detecta-informacion-perdida-en-estudios-radiologicos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/desarrollan-herramienta-de-inteligencia-artificial-que-detecta-informacion-perdida-en-estudios-radiologicos\/","title":{"rendered":"They develop an Artificial Intelligence tool that detects lost information in radiological studies"},"content":{"rendered":"
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Through the IBM Watson Health Digital Health tool, a white paper on lost information in radiology using Artificial Intelligence (AI) was published.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Missing information or findings is a relatively common and well-known problem in radiological studies. This problem is caused by various factors, such as reader fatigue, even a distraction by the professional in charge.<\/p>

For example, the detection of lung nodules through radiological images can be very complicated, since cancer detection programs are used for this, which can overload the system. In this sense, this can lead to erroneous results and even lawsuits against specialists for malpractice.<\/p>

The AI tool was developed through image and text algorithms that jointly analyze computed tomography (CT) studies and radiology reports.<\/p>

\u201cThe algorithm is designed to detect any non-calcified pulmonary nodule with a diameter greater than 6 mm. The text AI algorithm determines if any pulmonary nodules were mentioned in the associated clinical report. If a pulmonary nodule is identified on the imaging study, but is not mentioned as present in the text of the radiology report, the study is identified as having a missing potential pulmonary nodule,\u201d the study explains.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The research involved CT scans from hospitals in the UK and the US. Studies that were identified by the AI as potentially failing to detect lung nodules were reviewed by the American Board of Radiology (ABR). In this way, the imaging studies with nodules detected and the radiological studies without reported nodules were reviewed by specialists.<\/p>

\u201cThis study demonstrates the utility of our AI tool in identifying missed lung nodules in a large and diverse set of CT scans of the chest and abdomen that reflect a real clinical setting. Of a total of 32,134 studies, 100 true missed nodules were identified with only 315 false positives\u201d, concludes the study, which was presented during the AIMed summit.<\/p>

You can consult the full study at the following link: https:\/\/ai-med.io\/wp-content\/uploads\/2022\/05\/IBM_Watson_Health_Imaging_AI_Research_Missed_Lung_Nodules_White_Paper_1_1.pdf<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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AIMED<\/strong><\/p>

https:\/\/ai-med.io\/more-news\/new-study-ai-tool-detects-missed-findings-with-high-accuracy\/<\/a><\/p>

https:\/\/ai-med.io\/wp-content\/uploads\/2022\/05\/IBM_Watson_Health_Imaging_AI_Research_Missed_Lung_Nodules_White_Paper_1_1.pdf<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

A trav\u00e9s de la herramienta de Salud Digital IBM Watson Health, fue publicado un documento t\u00e9cnico sobre informaci\u00f3n perdida en radiolog\u00eda utilizando Inteligencia Artificial (IA). La informaci\u00f3n o hallazgos perdidos es un problema relativamente com\u00fan y conocido en los estudios radiol\u00f3gicos. Este problema es ocasionado por diversos factores, como la fatiga del lector, hasta una distracci\u00f3n por parte del profesional encargado. Por ejemplo, la detecci\u00f3n de n\u00f3dulos pulmonares a trav\u00e9s de im\u00e1genes radiol\u00f3gicas puede ser muy complicado, ya que para esto se utilizan programas de detecci\u00f3n de c\u00e1ncer, que pueden llegar a sobrecargar el sistema. En este sentido, esto puede llevar a resultados err\u00f3neos e incluso a demandas a los especialistas por mala praxis. La herramienta de IA fue desarrollada a trav\u00e9s de algoritmos de imagen y texto que analizan conjuntamente estudios de tomograf\u00eda computarizada (TC) e informes de radiolog\u00eda. \u201cEl algoritmo est\u00e1 dise\u00f1ado para detectar cualquier n\u00f3dulo pulmonar no calcificado con un di\u00e1metro superior a 6 mm. El algoritmo AI de texto determina si se mencion\u00f3 alg\u00fan n\u00f3dulo pulmonar en el informe cl\u00ednico asociado. Si se identifica un n\u00f3dulo pulmonar en el estudio de im\u00e1genes, pero no se menciona como presente en el texto del informe de radiolog\u00eda, se identifica que el estudio tiene un n\u00f3dulo pulmonar potencial perdido\u201d, explica el estudio. La investigaci\u00f3n cont\u00f3 con escaneos de TC de hospitales de Reino Unido y Estados Unidos. Los estudios que fueron identificados por la IA con un potencial fallo en la detecci\u00f3n de n\u00f3dulos pulmonares fueros revisados por el American Board of Radiology (ABR). De esta forma los estudios de im\u00e1genes con n\u00f3dulos detectados y los estudios radiol\u00f3gicos sin n\u00f3dulos reportados fueron revisados por especialistas. \u201cEste estudio demuestra la utilidad de nuestra herramienta de IA para identificar n\u00f3dulos pulmonares perdidos en un grupo grande y conjunto diverso de estudios de TC de t\u00f3rax y abdomen que reflejan un entorno cl\u00ednico real. De un total de 32,134 estudios, se identificaron 100 n\u00f3dulos verdaderos perdidos con solo 315 falsos positivos\u201d, concluye el estudio, que fue presentado durante la cumbre de AIMed. Puedes consultar el estudio completo en el siguiente enlace: https:\/\/ai-med.io\/wp-content\/uploads\/2022\/05\/IBM_Watson_Health_Imaging_AI_Research_Missed_Lung_Nodules_White_Paper_1_1.pdf BIBLIOGRAF\u00cdA AIMED https:\/\/ai-med.io\/more-news\/new-study-ai-tool-detects-missed-findings-with-high-accuracy\/ https:\/\/ai-med.io\/wp-content\/uploads\/2022\/05\/IBM_Watson_Health_Imaging_AI_Research_Missed_Lung_Nodules_White_Paper_1_1.pdf<\/p>","protected":false},"author":1,"featured_media":26619,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,160,3396],"tags":[145],"class_list":["post-26618","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-noticias","category-uso-de-plataformas-digitales","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/26618","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=26618"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/26618\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/26619"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=26618"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=26618"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=26618"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}